Today's navigation applications on mobile devices such as smart phones, desktop computers, or in-vehicle navigation systems assist users in devising optimum travel routes to destinations, help users understand vehicle orientation and direction of travel, illustrate various places of interest, and inform the driver about driving conditions on a road network (e.g., traffic density, weather, accidents, construction, etc.).
Some recent advances within the navigation systems include algorithms and interfaces that perform optimization of route planning according to carbon footprint (e.g., eco-driving route planning), battery efficiency (e.g., electric vehicle route planning), comfort (e.g., scenic route planning), and safety (e.g., avoid high crime area route planning). Others have proposed various systems and methods for determining the optimum route to a destination. However, such conventional systems are focused specifically on defining systems and methods for optimizing route plans with non-autonomous vehicles, meaning that routes are ultimately selected only through the physical decisions and actions of the user after taking into account historical data, weather conditions, traffic density, and a host of other variables that can impact travel time and/or distance. The conventional art, however, does not provide a system and method for planning and optimizing routes for semi-autonomous and/or fully autonomous vehicles, which limit and even eliminate the need for user interaction. Moreover, the conventional art does not factor in variables to calculate a percentage of a route that can be driven autonomously with a vehicle that has the requisite technology to do so.